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        Download the raw data used to create the plots in this report below:

        Note that additional data was saved in multiqc_data when this report was generated.


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        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

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        About MultiQC

        This report was generated using MultiQC, version 1.11

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/ewels/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        This report has been generated by the nf-core/quantms analysis pipeline. For information about how to interpret these results, please see the documentation.

        Report generated on 2022-07-25, 15:45 based on data in: /home/chengxin/pmultiqctest/test_spectrum_count


        pmultiqc

        pmultiqc is a multiQC module to show the pipeline performance of mass spectrometry based quantification pipelines such as nf-core/quantms.

        Experimental Design

        This table shows the design of the experiment. I.e., which files and channels correspond to which sample/condition/fraction.

        You can see details about it in https://abibuilder.informatik.uni-tuebingen.de/archive/openms/Documentation/release/latest/html/classOpenMS_1_1ExperimentalDesign.html

        Showing 6/6 rows and 6/6 columns.
        Spectra FileFraction_GroupFractionLabelSampleMSstats_ConditionMSstats_BioReplicate
        BSA1_F1.mzML111111
        BSA1_F2.mzML121111
        BSA2_F1.mzML211222
        BSA2_F2.mzML221222
        BSA3_F1.mzML311333
        BSA3_F2.mzML321333

        HeatMap

        This heatmap shows a performance overview of the pipeline

        This plot shows the pipeline performance overview. Some metrics are calculated.

        • Heatmap score[Contaminants]: as fraction of summed intensity with 0 = sample full of contaminants; 1 = no contaminants
        • Heatmap score[Pep Intensity (>23.0)]: Linear scale of the median intensity reaching the threshold, i.e. reaching 2^21 of 2^23 gives score 0.25.
        • Heatmap score[Charge]: Deviation of the charge 2 proportion from a representative Raw file (median). For typtic digests, peptides of charge 2 (one N-terminal and one at tryptic C-terminal R or K residue) should be dominant. Ionization issues (voltage?), in-source fragmentation, missed cleavages and buffer irregularities can cause a shift (see Bittremieux 2017, DOI: 10.1002/mas.21544 ).
        • Heatmap score [MC]: the fraction (0% - 100%) of fully cleaved peptides per Raw file
        • Heatmap score [MC Var]: each Raw file is scored for its deviation from the ‘average’ digestion state of the current study.
        • Heatmap score [ID rate over RT]: Judge column occupancy over retention time. Ideally, the LC gradient is chosen such that the number of identifications (here, after FDR filtering) is uniform over time, to ensure consistent instrument duty cycles. Sharp peaks and uneven distribution of identifications over time indicate potential for LC gradient optimization.Scored using ‘Uniform’ scoring function. i.e. constant receives good score, extreme shapes are bad.
        • Heatmap score [MS2 Oversampling]: The percentage of non-oversampled 3D-peaks. An oversampled 3D-peak is defined as a peak whose peptide ion (same sequence and same charge state) was identified by at least two distinct MS2 spectra in the same Raw file. For high complexity samples, oversampling of individual 3D-peaks automatically leads to undersampling or even omission of other 3D-peaks, reducing the number of identified peptides.
        • Heatmap score [Pep Missing]: Linear scale of the fraction of missing peptides.
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        Summary Table

        This table shows the quantms pipeline summary statistics

        This table shows the quantms pipeline summary statistics

        Showing 1/1 rows and 5/5 columns.
        #MS2 Spectra#Identified MS2 Spectra%Identified MS2 Spectra#Peptides Identified#Proteins Identified#Proteins Quantified
        3136
        199
        6.35%
        55
        23
        22

        Pipeline Result Statistics

        This plot shows the quantms pipeline final result

        This plot shows the quantms pipeline final result. Including Sample Name、Possible Study Variables、identified the number of peptide in the pipeline、 and identified the number of modified peptide in the pipeline, eg. All data in this table are obtained from the out_msstats file. You can also remove the decoy with the remove_decoy parameter.

        Showing 6/6 rows and 7/7 columns.
        Spectra FileSample NameConditionFraction#Peptide IDs#Unambiguous Peptide IDs#Modified Peptide IDs#Protein (group) IDs
        BSA1_F1.mzML
        1
        1
        1
        26
        24
        11
        10
        BSA1_F2.mzML
        1
        1
        2
        16
        15
        6
        5
        BSA2_F1.mzML
        2
        2
        1
        26
        24
        12
        10
        BSA2_F2.mzML
        2
        2
        2
        16
        16
        6
        5
        BSA3_F1.mzML
        3
        3
        1
        17
        16
        9
        5
        BSA3_F2.mzML
        3
        3
        2
        9
        9
        3
        4

        Number of Peptides Per Protein

        This plot shows the number of peptides per proteins in quantms pipeline final result

        This statistic is extracted from the out_msstats file. Proteins supported by more peptide identifications can constitute more confident results.

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        Spectra Tracking

        This plot shows the tracking of the number of spectra along the quantms pipeline

        This table shows the changes in the number of spectra corresponding to each input file during the pipeline operation. And the number of peptides finally identified and quantified is obtained from the PSM table in the mzTab file. You can also remove decoys with the remove_decoy parameter.:

        • MS1_Num: The number of MS1 spectra extracted from mzMLs
        • MS2_Num: The number of MS2 spectra extracted from mzMLs
        • MSGF: The Number of spectra identified by MSGF search engine
        • Comet: The Number of spectra identified by Comet search engine
        • PSMs from quant. peptides: extracted from PSM table in mzTab file

        • Peptides quantified: extracted from PSM table in mzTab file

        Showing 6/6 rows and 5/5 columns.
        Spectra File#MS1 Spectra#MS2 SpectraMSGF#PSMs from quant. peptides#Peptides quantified
        BSA1_F1.mzML
        286
        481
        207
        59
        25
        BSA1_F2.mzML
        278
        639
        295
        35
        16
        BSA2_F1.mzML
        257
        557
        219
        34
        26
        BSA2_F2.mzML
        267
        609
        250
        31
        16
        BSA3_F1.mzML
        290
        383
        157
        25
        16
        BSA3_F2.mzML
        298
        467
        196
        15
        9

        Distribution of precursor charges

        This is a bar chart representing the distribution of the precursor ion charges for a given whole experiment.

        This information can be used to identify potential ionization problems including many 1+ charges from an ESI ionization source or an unexpected distribution of charges. MALDI experiments are expected to contain almost exclusively 1+ charged ions. An unexpected charge distribution may furthermore be caused by specific search engine parameter settings such as limiting the search to specific ion charges.

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        Number of Peaks per MS/MS spectrum

        This chart represents a histogram containing the number of peaks per MS/MS spectrum in a given experiment. This chart assumes centroid data. Too few peaks can identify poor fragmentation or a detector fault, as opposed to a large number of peaks representing very noisy spectra. This chart is extensively dependent on the pre-processing steps performed to the spectra (centroiding, deconvolution, peak picking approach, etc).

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        Peak Intensity Distribution

        This is a histogram representing the ion intensity vs. the frequency for all MS2 spectra in a whole given experiment. It is possible to filter the information for all, identified and unidentified spectra. This plot can give a general estimation of the noise level of the spectra.

        Generally, one should expect to have a high number of low intensity noise peaks with a low number of high intensity signal peaks. A disproportionate number of high signal peaks may indicate heavy spectrum pre-filtering or potential experimental problems. In the case of data reuse this plot can be useful in identifying the requirement for pre-processing of the spectra prior to any downstream analysis. The quality of the identifications is not linked to this data as most search engines perform internal spectrum pre-processing before matching the spectra. Thus, the spectra reported are not necessarily pre-processed since the search engine may have applied the pre-processing step internally. This pre-processing is not necessarily reported in the experimental metadata.

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        Oversampling Distribution

        An oversampled 3D-peak is defined as a peak whose peptide ion (same sequence and same charge state) was identified by at least two distinct MS2 spectra in the same Raw file.

        For high complexity samples, oversampling of individual 3D-peaks automatically leads to undersampling or even omission of other 3D-peaks, reducing the number of identified peptides. Oversampling occurs in low-complexity samples or long LC gradients, as well as undersized dynamic exclusion windows for data independent acquisitions.

                    * Heatmap score [EVD: MS2 Oversampling]: The percentage of non-oversampled 3D-peaks.
        
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        Delta Mass

        This chart represents the distribution of the relative frequency of experimental precursor ion mass (m/z) - theoretical precursor ion mass (m/z).

        Mass deltas close to zero reflect more accurate identifications and also that the reporting of the amino acid modifications and charges have been done accurately. This plot can highlight systematic bias if not centered on zero. Other distributions can reflect modifications not being reported properly. Also it is easy to see the different between the target and the decoys identifications.

        loading..

        Protein Quantification Table

        This plot shows the quantification information of proteins in the final result (mainly the mzTab file).

        The quantification information (Spectral Counting) of proteins is obtained from the mzTab file. The table shows the quantitative level and distribution of proteins in different study variables and run.

        • Peptides_Number: The number of peptides for each protein.
        • Average Spectral Counting: Average spectral counting of each protein across all conditions with NA=0 or NA ignored.
        • Spectral Counting in each condition (Eg. CT=Mixture;CN=UPS1;QY=0.1fmol): Average spectral counting of replicates.

        Click Show replicates to switch to bar plots of counting in each replicate.

        Showing 22/22 rows and 8/8 columns.
        ProteinNamePeptides_NumberAverage Spectrum Counting123123
        P02769|ALBU_BOVIN
        28
        52
        73
        49
        33
        P00761|TRYP_PIG
        2
        3
        5
        2
        1
        sp|O46375|TTHY_BOVIN
        5
        4
        5
        4
        2
        tr|A9GKZ3|A9GKZ3_SORC5
        1
        1
        0
        1
        0
        P46406|G3P_RABIT
        1
        2
        0
        2
        0
        tr|A9FKB2|A9FKB2_SORC5
        1
        1
        0
        0
        1
        Q15323|K1H1_HUMAN
        1
        1
        1
        1
        0
        P62739|ACTA_BOVIN
        2
        2
        2
        0
        0
        tr|A9GCS7|A9GCS7_SORC5
        1
        1
        0
        0
        1
        tr|A9FS16|A9FS16_SORC5
        1
        1
        1
        0
        0
        tr|A9GEZ4|A9GEZ4_SORC5
        1
        1
        0
        1
        0
        tr|A9GCG3|A9GCG3_SORC5
        1
        1
        0
        0
        1
        tr|A9FDH1|A9FDH1_SORC5
        1
        1
        0
        1
        0
        tr|A9GMF9|A9GMF9_SORC5
        1
        1
        0
        1
        0
        tr|A9F0I2|A9F0I2_SORC5
        1
        1
        0
        1
        0
        tr|A9GHP5|A9GHP5_SORC5
        1
        1
        0
        1
        0
        tr|A9FBB1|A9FBB1_SORC5
        1
        1
        1
        0
        0
        tr|A9ERQ5|A9ERQ5_SORC5
        1
        1
        1
        0
        0
        tr|A9FJQ1|A9FJQ1_SORC5
        1
        1
        1
        0
        0
        sp|A9FU01|T23O_SORC5
        1
        1
        1
        0
        0
        tr|A9GE86|A9GE86_SORC5
        1
        1
        1
        0
        0
        tr|A9FP67|A9FP67_SORC5
        1
        1
        1
        0
        0
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        nf-core/quantms Software Versions

        are collected at run time from the software output.

        Process Name Software Version
        CUSTOM_DUMPSOFTWAREVERSIONS python 3.9.5
        yaml 5.4.1
        FDRIDPEP FalseDiscoveryRate 2.8.0-pre-exported-20220314 Mar 14 2022, 18:56:49
        IDFILTER IDFilter 2.8.0-pre-exported-20220314 Mar 14 2022, 18:56:49
        IDPEP IDPosteriorErrorProbability 2.8.0-pre-exported-20220314 Mar 14 2022, 18:56:49
        IDSCORESWITCHER IDScoreSwitcher 2.8.0-pre-exported-20220314 Mar 14 2022, 18:56:49
        MZMLINDEXING FileConverter 2.8.0-pre-exported-20220314 Mar 14 2022, 18:56:49
        PROTEOMICSLFQ ProteomicsLFQ 2.8.0-pre-exported-20220314 Mar 14 2022, 18:56:49
        SAMPLESHEET_CHECK sdrf-pipelines 0.0.21
        SEARCHENGINEMSGF MSGFPlusAdapter 2.8.0-pre-exported-20220227 Feb 27 2022, 20:31:47
        msgf_plus MS-GF+ Release (v2021.03.22) (22 March 2021)
        Workflow Nextflow 21.10.6
        nf-core/quantms 1.1dev

        nf-core/quantms Workflow Summary

        - this information is collected when the pipeline is started.

        Core Nextflow options

        runName
        hungry_linnaeus
        containerEngine
        docker
        launchDir
        /home/chengxin/newPR/quantms
        workDir
        /home/chengxin/newPR/quantms/work
        projectDir
        /home/chengxin/newPR/quantms
        userName
        chengxin
        profile
        test_lfq,docker
        configFiles
        /home/chengxin/newPR/quantms/nextflow.config

        Input/output options

        input
        /home/chengxin/lfq_testdata/BSA_design_urls1.tsv
        outdir
        ./results_lfq

        Protein database

        database
        /home/chengxin/lfq_testdata/18Protein_SoCe_Tr_detergents_trace_target_decoy.fasta
        decoy_string
        rev

        Database search

        search_engines
        msgf
        instrument
        N/A

        Modification localization

        luciphor_debug
        N/A

        PSM re-scoring (general)

        posterior_probabilities
        fit_distributions
        run_fdr_cutoff
        0.10

        PSM re-scoring (Percolator)

        description_correct_features
        N/A

        Consensus ID

        consensusid_considered_top_hits
        N/A
        min_consensus_support
        N/A

        Isobaric analyzer

        select_activation
        HCD

        Protein inference

        protein_level_fdr_cutoff
        1.0
        psm_level_fdr_cutoff
        1.0

        Protein Quantification (DDA)

        labelling_type
        label free sample
        ratios
        N/A
        normalize
        N/A
        fix_peptides
        N/A

        DIA-NN

        mass_acc_ms2
        13
        mass_acc_ms1
        7
        scan_window
        8

        Statistical post-processing

        contrasts
        pairwise
        add_triqler_output
        true

        Quality control

        enable_pmultiqc
        true

        Institutional config options

        config_profile_name
        Test profile for DDA LFQ
        config_profile_description
        Minimal test dataset to check pipeline function of the label-free quantification branch of the pipeline

        Max job request options

        max_cpus
        2
        max_memory
        6 GB
        max_time
        2d

        Generic options

        hostnames
        N/A